OpenCV is a library of programming functions mainly aimed at real-time computer vision. This course will show you how machine learning is great choice to solve real-word computer vision problems and how you can use the OpenCV modules to implement the popular machine learning concepts.
The course will teach you how to work with the various OpenCV modules for statistical modelling and machine learning. You will start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to implement them with the help of real-world examples. The course will also show you how you can implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C++ and OpenCV.
Master Logistic Regression and regularization techniques
Understand supervised and unsupervised machine learning algorithms
Implement OpenCV's functionalities in machine learning algorithms
Solve image segmentation problem using K-Means Clustering
Get to know the key elements of a neural network and deep learning and its ability to learn
Load models trained with popular deep learning libraries such as Caffe
Prior knowledge of computer vision and OpenCV is required.
Who is this course intended for?
If you have a basic working knowledge of computer vision and OpenCV, and want to perform machine learning with OpenCV, this course is for you. Some understanding of statistical concepts would be helpful, but is not mandatory.